Buggy code:
public class AdaptiveLogisticRegression implements OnlineLearner { private static final int SURVIVORS = 2 ; private int record ; private int cutoff = 1000 ; private int minInterval = 1000 ; private int maxInterval = 1000 ; private int currentStep = 1000 ; private int bufferSize = 1000 ; private List < AdaptiveLogisticRegression . TrainingExample > buffer = Lists . newArrayList ( ) ; private EvolutionaryProcess < AdaptiveLogisticRegression . Wrapper > ep ; private State < AdaptiveLogisticRegression . Wrapper > best ; private int threadCount = 20 ; private int poolSize = 20 ; private State < AdaptiveLogisticRegression . Wrapper > seed ; private int numFeatures ; private boolean freezeSurvivors = true ; private AdaptiveLogisticRegression ( ) { } public AdaptiveLogisticRegression ( int numCategories , int numFeatures , PriorFunction prior ) { } @ Override public void train ( int actual , Vector instance ) { } @ Override public void train ( long trackingKey , int actual , Vector instance ) { } @ Override public void train ( long trackingKey , String groupKey , int actual , Vector instance ) { } private void trainWithBufferedExamples ( ) { try { this . best = ep . parallelDo ( new EvolutionaryProcess . Function < AdaptiveLogisticRegression . Wrapper > ( ) { @ Override public double apply ( Wrapper x , double [ ] params ) { for ( TrainingExample example : buffer ) { x . train ( example ) ; } if ( x . getLearner ( ) . validModel ( ) ) { if ( ( x . getLearner ( ) . numCategories ( ) ) == 2 ) { return x . wrapped . auc ( ) ; } else { return x . wrapped . logLikelihood ( ) ; } } else { return Double . NaN ; } } } ) ; } catch ( InterruptedException e ) { } catch ( ExecutionException e ) { <START_BUG> throw new IllegalStateException ( e ) ; <END_BUG> } buffer . clear ( ) ; if ( ( record ) > ( cutoff ) ) { cutoff = nextStep ( record ) ; ep . mutatePopulation ( AdaptiveLogisticRegression . SURVIVORS ) ; if ( freezeSurvivors ) { for ( State < AdaptiveLogisticRegression . Wrapper > state : ep . getPopulation ( ) . subList ( 0 , AdaptiveLogisticRegression . SURVIVORS ) ) { state . getPayload ( ) . freeze ( state ) ; } } } } public int nextStep ( int recordNumber ) { } public static int stepSize ( int recordNumber , double multiplier ) { } @ Override public void close ( ) { } public void setInterval ( int interval ) { } public void setInterval ( int minInterval , int maxInterval ) { } public void setPoolSize ( int poolSize ) { } public void setThreadCount ( int threadCount ) { } public void setAucEvaluator ( OnlineAuc auc ) { } private void setupOptimizer ( int poolSize ) { } public int numFeatures ( ) { } public double auc ( ) { } public State < AdaptiveLogisticRegression . Wrapper > getBest ( ) { } public void setBest ( State < AdaptiveLogisticRegression . Wrapper > best ) { } public int getRecord ( ) { } public void setRecord ( int record ) { } public int getMinInterval ( ) { } public int getMaxInterval ( ) { } public int getNumCategories ( ) { } public PriorFunction getPrior ( ) { } public void setBuffer ( List < AdaptiveLogisticRegression . TrainingExample > buffer ) { } public List < AdaptiveLogisticRegression . TrainingExample > getBuffer ( ) { } public EvolutionaryProcess < AdaptiveLogisticRegression . Wrapper > getEp ( ) { } public void setEp ( EvolutionaryProcess < AdaptiveLogisticRegression . Wrapper > ep ) { } public State < AdaptiveLogisticRegression . Wrapper > getSeed ( ) { } public void setSeed ( State < AdaptiveLogisticRegression . Wrapper > seed ) { } public int getNumFeatures ( ) { } public void setAveragingWindow ( int averagingWindow ) { } public void setFreezeSurvivors ( boolean freezeSurvivors ) { } public static class Wrapper implements Payload < AdaptiveLogisticRegression . Wrapper > { private CrossFoldLearner wrapped ; private Wrapper ( ) { } public Wrapper ( int numCategories , int numFeatures , PriorFunction prior ) { } @ Override public AdaptiveLogisticRegression . Wrapper copy ( ) { } @ Override public void update ( double [ ] params ) { } public void freeze ( State < AdaptiveLogisticRegression . Wrapper > s ) { } public void setMappings ( State < AdaptiveLogisticRegression . Wrapper > x ) { } public void train ( AdaptiveLogisticRegression . TrainingExample example ) { } public CrossFoldLearner getLearner ( ) { } @ Override public String toString ( ) { } public void setAucEvaluator ( OnlineAuc auc ) { } } public static class TrainingExample { private long key ; private String groupKey ; private int actual ; private Vector instance ; private TrainingExample ( ) { } public TrainingExample ( long key , String groupKey , int actual , Vector instance ) { } public long getKey ( ) { } public int getActual ( ) { } public Vector getInstance ( ) { } public String getGroupKey ( ) { } } }

Fix:
throw new IllegalStateException ( e . getCause ( ) ) ;